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2 More Big Data V’s — Value And Veracity

A lot has been written on the 3 V’s of Big Data – Volume, Variety, and Velocity. Yet there are two more equally, and perhaps even more important, attributes to consider – Value (business value to be derived) and Veracity (the quality and understandability of the data).

Value

Value starts and ends with the business use case. The business must define the analytic application of the data and its potential associated value to the business. Use cases are important both to define initial “Big Data” pilot justification and to build a road map for transformation.

This value is critical in the initial business case to establish your environment and to sustain ongoing investments. After project implementation, you also should document and socialize the realized value. This validates the investment return on investment (ROI) and promotes future funding.

Yet many companies struggle with defining effective Big Data use cases. Business users need to think about how the unique characteristics of Big Data (real time, sentiment, predictive, and mobility) are factors that can transform their business processes from a strategic view in revenue, reputation, and customer engagement.

Strong use cases include revenue generation related ones such as pricing, or revenue leakage ones such as fraud, which require real time analytics. Reputation and brand value are greatly influenced and even driven by customer and partner sentiment. Customer acquisition, retention, and growth all rely heavily on predictive modeling and are greatly enhanced by new Big Data characteristics. Mobility is not only important for customers who shop and buy in real time, but it also enables your partners, sales, and service staff to detect and diffuse issues before they impact customer satisfaction and retention.

To be sure, there are many valid Big Data use cases for expense reduction, but the ones that will bring most value your business are on the revenue side.

Start using the data you have today, document its source and intended use and any “nuances,” make the metadata and definitions visible not just to developers but to the business. Make sure that your data dictionary tool has a user-friendly end-user interface.

As users begin to use the data, they become truly engaged and more willing to invest in efforts to clean up data—ideally at the source. Through increased use they’ll also identify needs for additional data, either defining requirements to add to internal source systems or identifying needs from partner or public third party data.

So as you’re plotting your Big Data road map, be sure to expand your vision to include both Value and Veracity.